Estimation of the signal subspace without estimation of the inverse covariance matrix
dc.bibliographicCitation.seriesTitle | WIAS Preprints | eng |
dc.bibliographicCitation.volume | 1546 | |
dc.contributor.author | Panov, Vladimir A. | |
dc.date.accessioned | 2016-03-24T17:39:00Z | |
dc.date.available | 2019-06-28T08:07:57Z | |
dc.date.issued | 2010 | |
dc.description.abstract | Let a high-dimensional random vector $vecX$ can be represented as a sum of two components - a signal $vecS$, which belongs to some low-dimensional subspace $mathcalS$, and a noise component $vecN$. This paper presents a new approach for estimating the subspace $mathcalS$ based on the ideas of the Non-Gaussian Component Analysis. Our approach avoids the technical difficulties that usually exist in similar methods - it doesn't require neither the estimation of the inverse covariance matrix of $vecX$ nor the estimation of the covariance matrix of $vecN$. | eng |
dc.description.version | publishedVersion | eng |
dc.format | application/pdf | |
dc.identifier.issn | 0946-8633 | |
dc.identifier.uri | https://doi.org/10.34657/2337 | |
dc.identifier.uri | https://oa.tib.eu/renate/handle/123456789/2543 | |
dc.language.iso | eng | eng |
dc.publisher | Berlin : Weierstraß-Institut für Angewandte Analysis und Stochastik | eng |
dc.relation.issn | 0946-8633 | eng |
dc.rights.license | This document may be downloaded, read, stored and printed for your own use within the limits of § 53 UrhG but it may not be distributed via the internet or passed on to external parties. | eng |
dc.rights.license | Dieses Dokument darf im Rahmen von § 53 UrhG zum eigenen Gebrauch kostenfrei heruntergeladen, gelesen, gespeichert und ausgedruckt, aber nicht im Internet bereitgestellt oder an Außenstehende weitergegeben werden. | ger |
dc.subject.ddc | 510 | eng |
dc.subject.other | Dimension reduction | eng |
dc.subject.other | non-Gaussian components | eng |
dc.subject.other | NGCA | eng |
dc.title | Estimation of the signal subspace without estimation of the inverse covariance matrix | eng |
dc.type | Report | eng |
dc.type | Text | eng |
tib.accessRights | openAccess | eng |
wgl.contributor | WIAS | eng |
wgl.subject | Mathematik | eng |
wgl.type | Report / Forschungsbericht / Arbeitspapier | eng |
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